This file provides information for replicating the results from: Jonathan P. Kastellec and Jeffrey Lax. 2008.   "Case Selection and the Study of Judicial Politics. Journal of Empirical Legal Studies. 5(3): 407-446.



DATA

-- The non-compliance puzzle discussed in the paper is based on the data analyzed in Klein and Hume's 2003 paper, "Fear of Reversal as an Explanation of Lower Court Compliance."    This dataset is available in "Fear of Reversal Data.dta"

-- The analyses in remainder of the paper are based on the data used in
Songer et al's 1994 paper, "The Hierarchy of Justice: Testing a
Principal-Agent Model of Supreme Court-Circuit Court Interaction." (Note
that the data used in a Klein and Hume paper is a subset of this
data).  The base version of this dataset is available in: "songer_original_data.dta." As explained in the paper, we then appended five copies of this dataset together, which we then used to run our simulations.  That dataset is available in "simulation_data_base.dta"

STATA DO-FILES AND DATASETS FOR ANALYSIS

With the data in hand, we then programmed simulations in Stata, creating
one do-file for each selection strategy analyzed in the paper:

-- All Cases: "simulation_all_cases_final.do"
-- Random Selection: "simulation_random_final.do"
-- Close Cases: "simulation_close_all_final.do"
-- Close Liberal Cases: "simulation_close_liberal_final.do"
-- Close Conservative Cases: "simulation_close_conservative_final.do"
-- Anomalous Cases: "simulation_anomalous_all_final.do"
-- Anomalous Liberal Cases: "simulation_anomalous_liberal_final.do"
-- Anomalous Conservative Cases: "simulation_anomalous_conservative_final.do"
-- Conservative, Non-Excepted Home: "simulation_home_no_except_final.do"

Running each of these do-files produces a new dataset of 1,000
observations, 1 for each simulation.  The datasets we used in the paper
are below.  Note that if you re-run the do-files, the resulting dataset
will be slightly different to the random draws utilized in the simulations.

-- All Cases: "sim_results_all_cases.dta

-- Random Selection: "sim_results_random.dta"

-- Close Cases: "sim_results_close_all.dta"

-- Close Liberal Cases: "sim_results_close_liberal.dta"

-- Close Conservative Cases: "sim_results_close_conservative.dta"

-- Anomalous Cases: "sim_results_anomalous_all.dta"

-- Anomalous Liberal Cases: "sim_results_anomalous_liberal.dta"

-- Anomalous Conservative Cases: "sim_results_anomalous_conservative.dta"

-- Conservative, Non-Excepted Home: "sim_results_home_no_except.dta"

R CODE FOR ANALYSES IN PAPER

Finally, with the simulated data at hand, we analyzed it using R,
which we used to construct all the graphs that appear in the paper. 
Complete code is available in "R_script_complete.R".

 

 

